Packing and covering semidefinite programs (SDPs) appear in natural relaxations of many combinatorial optimization problems as well as a number of other applications. Recently, several techniques were proposed, that utilize the particular structure of this class of problems, to obtain more efficient algorithms than those offered by general SDP solvers. For certain applications, such as those described in this paper, it maybe required to deal with SDP\u27s with exponentially or infinitely many constraints, which are accessible only via an oracle. In this paper, we give an efficient primal-dual algorithm to solve the problem in this case, which is an extension of a logarithmic-potential based algorithm of Grigoriadis, Khachiyan, Porkolab and ...
We consider low-rank semidefinite programming (LRSDP) relaxations of unconstrained {−1, 1} quadrati...
In this paper a symmetric primal-dual transformation for positive semidefinite programming is propos...
Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking po...
We employ chordal decomposition to reformulate a large and sparse semidefinite program (SDP), either...
Semidefinite programs (SDP) have been used in many recent approximation algorithms. We develop a gen...
Semidefinite programs (SDPs) originating from the Kalman-Yakubovich-Popov lemma often have a large n...
The Semidefinite Program (SDP) is a fundamental problem in mathematical programming. It covers a wid...
International audienceWe introduce a new class of algorithms for solving linear semidefinite program...
Combinatorial optimization problems such as routing, scheduling, covering and packing problems aboun...
Recently, M. Bouafoa, et al. (Journal of optimization Theory and Applications, August, 2016), invest...
We consider linear programming in the oracle model: mincT x s.t. x ∊ P, where the polyhedron P = {x ...
This thesis focuses on two topics in the field of convex optimization: preprocessing algorithms for ...
Many semidefinite programs (SDPs) arising in practical applications have useful structural propertie...
We build upon the work of Fukuda et al. [9] and Nakata et al. [26], in which the theory of partial p...
We study the design of polylogarithmic depth algorithms for approximately solving packing and coveri...
We consider low-rank semidefinite programming (LRSDP) relaxations of unconstrained {−1, 1} quadrati...
In this paper a symmetric primal-dual transformation for positive semidefinite programming is propos...
Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking po...
We employ chordal decomposition to reformulate a large and sparse semidefinite program (SDP), either...
Semidefinite programs (SDP) have been used in many recent approximation algorithms. We develop a gen...
Semidefinite programs (SDPs) originating from the Kalman-Yakubovich-Popov lemma often have a large n...
The Semidefinite Program (SDP) is a fundamental problem in mathematical programming. It covers a wid...
International audienceWe introduce a new class of algorithms for solving linear semidefinite program...
Combinatorial optimization problems such as routing, scheduling, covering and packing problems aboun...
Recently, M. Bouafoa, et al. (Journal of optimization Theory and Applications, August, 2016), invest...
We consider linear programming in the oracle model: mincT x s.t. x ∊ P, where the polyhedron P = {x ...
This thesis focuses on two topics in the field of convex optimization: preprocessing algorithms for ...
Many semidefinite programs (SDPs) arising in practical applications have useful structural propertie...
We build upon the work of Fukuda et al. [9] and Nakata et al. [26], in which the theory of partial p...
We study the design of polylogarithmic depth algorithms for approximately solving packing and coveri...
We consider low-rank semidefinite programming (LRSDP) relaxations of unconstrained {−1, 1} quadrati...
In this paper a symmetric primal-dual transformation for positive semidefinite programming is propos...
Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking po...